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Qwen3.8-Next model achieves faster performance on M5 hardware

By PulseAugur Editorial · [1 sources] ·

A user has successfully adapted their streaming stack to run the Qwen3.8-Next model, achieving faster performance than a dense 27b model on their M5 hardware. The Qwen3.8-Next model, when run in a 3-bit quantized version, demonstrated 150 tps for prefill and 3.6 tps for decoding. This performance surpasses the 70 tps prefill and 3 tps decode of the 4-bit dense 27b model on the same M5 hardware. AI

IMPACT Demonstrates improved performance for open-source models on consumer hardware, potentially lowering barriers to entry for local LLM deployment.

RANK_REASON User benchmark of an open-source model on consumer hardware.

Read on r/LocalLLaMA →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Qwen3.8-Next model achieves faster performance on M5 hardware

How we ranked this

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6 / 100
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Newsworthiness bucket
Tool
User benchmark of an open-source model on consumer hardware.
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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model release
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High
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Breaking (< 6h)
Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

Full methodology in our editorial standards.

COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/maddie-lovelace ·

    Qwen3.8-Next streaming - 150tps prefill, 3.6 tps decode on M5 Air

    <!-- SC_OFF --><div class="md"><p>Out of curiosity, I thought I'd see if I could adapt my <a href="https://www.reddit.com/r/LocalLLaMA/comments/1vjm6dn/300b_on_32gb_moestreaming_findings_optimisations/">DSv4 streaming stack</a> from a few weeks ago to take Qwen3.8-Next. It worked…

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